Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Restarted Bayesian Online Change-point Detector achieves Optimal Detection Delay
Authors: Reda Alami, Odalric Maillard, Raphael Feraud
ICML 2020 | Venue PDF | LLM Run Details | Input Tokens: 19,098 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,875 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on synthetic and realworld data show that this proposal outperforms the state-of-art change-point detection strategy |
| Researcher Affiliation | Collaboration | 1Total S.A. 2INRIA-SCOOL Team 3Orange Labs. |
| Pseudocode | Yes | Algorithm 1 BOCPD (Fearnhead & Liu, 2007); Algorithm 2 R-BOCPD |
| Open Source Code | Yes | Software and simulation code is available at https://github.com/Ralami1859/Restarted-BOCPD. |
| Open Datasets | Yes | These data have been studied in the context of change-point detection by (Fearnhead & Clifford, 2003) and has become a benchmark data set for uni-variate changepoint detection. |
| Dataset Splits | No | No explicit details on train/validation/test splits, percentages, or cross-validation were provided for the datasets used. |
| Hardware Specification | No | No specific hardware (e.g., CPU, GPU models, or cloud instance types) used for experiments was mentioned. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., library or solver names with versions) were provided. |
| Experiment Setup | Yes | In all the experiment, we choose ηr,s,t = 1 nr:t for RBOCPD and h = 3/1200 for BOCPD. The curves are averaged over 300 runs. (Their error bars are also plotted). The parameter δ (false alarm rate of Imp GLR) is set to 0.01. |